Effect of probiotic bacteria on porcine rotavirus OSU infection of porcine intestinal epithelial IPEC-J2 cells
Bibliographic record
Abstract
Abstract Rotavirus infections in nursing or post-weaning piglets are known to cause diarrhea, which can lead to commercial losses. Probiotic supplementation is used as a prophylactic or therapeutic approach to dealing with microbial infections in humans and animals. To evaluate the effect of probiotic bacteria on porcine rotavirus infections, non-transformed porcine intestinal epithelial IPEC-J2 cells were used as an in vitro model, and three different procedures were tested. When cells were exposed to seven probiotics at concentrations of 105, 106, or 107 CFU/mL for 16 h and removed before rotavirus challenge, infection reduction rates determined by flow cytometry were as follows: 15% (106) and 18% (105) for Bifidobacterium longum R0175, 15% (107) and 16% (106) for B. animalis lactis A026, and 15% (105) for Lactobacillus plantarum 299V. When cells were exposed to three selected probiotic strains for 1 h at higher concentrations, that is, 108 and 5 × 108 CFU/mL, before infection with rotavirus, no significant reduction was observed. When the probiotic bacteria were incubated with the virus before cell infection, a significant 14% decrease in the infection rate was observed for B. longum R0175. The results obtained using a cell-probiotics-virus platform combined with flow cytometry analysis suggest that probiotic bacteria can have a protective effect on IPEC-J2 cells before infection and can also prevent rotavirus infection of the cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".